Endoscopic computer vision contests, such as the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge [1]} represent important and necessary efforts to encourage the development of robust models for surgical instrument segmentation. They integrate the developments in computer-assisted surgeries and benchmark the generalization capabilities of the developed methods on different clinical scenarios. Furthermore, they provide large-high-quality datasets to overcome one of the main bottlenecks of developing robust methodologies, which is the lack of annotated data.
